




"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


lowerCAmelCase				:     str					=				get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
					lowerCAmelCase				:     Tuple					=				json.load(f)






@require_torch
class        __magic_name__				(			unittest.TestCase							):






				'''simple docstring'''





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							return FSMTTokenizer.from_pretrained(_a					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   FSMTForConditionalGeneration.from_pretrained(_a					).to(_a					)
							if torch_device == "cuda":
										model.half()
							return model





				@parameterized.expand(
				    [
				        ["""en-ru""", 26.0],
				        ["""ru-en""", 22.0],
				        ["""en-de""", 22.0],
				        ["""de-en""", 29.0],
				    ]					)
				@slow
				def   _lowerCAmelCase							(			self						,					_a						,					_a					):





							"""simple docstring"""
							# note: this test is not testing the best performance since it only evals a small batch
							# but it should be enough to detect a regression in the output quality
							lowerCamelCase      =   f'facebook/wmt19-{pair}'
							lowerCamelCase      =   self.get_tokenizer(_a					)
							lowerCamelCase      =   self.get_model(_a					)

							lowerCamelCase      =   bleu_data[pair]["""src"""]
							lowerCamelCase      =   bleu_data[pair]["""tgt"""]

							lowerCamelCase      =   tokenizer(_a						,					return_tensors="""pt"""						,					truncation=_a						,					padding="""longest"""					).to(_a					)
							lowerCamelCase      =   model.generate(
							    input_ids=batch.input_ids						,					num_beams=8						,					)
							lowerCamelCase      =   tokenizer.batch_decode(
							    _a						,					skip_special_tokens=_a						,					clean_up_tokenization_spaces=_a					)
							lowerCamelCase      =   calculate_bleu(_a						,					_a					)
							print(_a					)
							self.assertGreaterEqual(scores["""bleu"""]						,					_a					)









"""simple docstring"""




from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map



def        a__					(							snake_case__							)		->   Dict[str, torch.Tensor]:
			lowerCamelCase      =   []
			lowerCamelCase      =   []
			lowerCamelCase      =   []

			for rt in rc.restypes:
						lowerCamelCase      =   rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
						restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]							)
						lowerCamelCase      =   {name: i for i, name in enumerate(snake_case__							)}
						restype_atomaa_to_atomaa_list.append(
						    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]							)

						restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]							)

			# Add dummy mapping for restype 'UNK'
			restype_atomaa_to_atomaa_list.append([0] * 14							)
			restype_atomaa_to_atomaa_list.append([0] * 37							)
			restype_atomaa_mask_list.append([0.0] * 14							)

			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.intaa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.intaa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.floataa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   protein["""aatype"""].to(torch.long							)

			# create the mapping for (residx, atom14) --> atom37, i.e. an array
			# with shape (num_res, 14) containing the atom37 indices for this protein
			lowerCamelCase      =   restype_atomaa_to_atomaa[protein_aatype]
			lowerCamelCase      =   restype_atomaa_mask[protein_aatype]

			lowerCamelCase      =   residx_atomaa_mask
			lowerCamelCase      =   residx_atomaa_to_atomaa.long()

			# create the gather indices for mapping back
			lowerCamelCase      =   restype_atomaa_to_atomaa[protein_aatype]
			lowerCamelCase      =   residx_atomaa_to_atomaa.long()

			# create the corresponding mask
			lowerCamelCase      =   torch.zeros([21, 37]			,					dtype=torch.floataa			,					device=protein["""aatype"""].device							)
			for restype, restype_letter in enumerate(rc.restypes							):
						lowerCamelCase      =   rc.restype_atoa[restype_letter]
						lowerCamelCase      =   rc.residue_atoms[restype_name]
						for atom_name in atom_names:
									lowerCamelCase      =   rc.atom_order[atom_name]
									lowerCamelCase      =   1

			lowerCamelCase      =   restype_atomaa_mask[protein_aatype]
			lowerCamelCase      =   residx_atomaa_mask

			return protein



def        a__					(							snake_case__							)		->   Dict[str, np.ndarray]:
			lowerCamelCase      =   tree_map(lambda snake_case__							: torch.tensor(snake_case__			,					device=batch["""aatype"""].device							)			,					snake_case__			,					np.ndarray							)
			lowerCamelCase      =   tensor_tree_map(lambda snake_case__							: np.array(snake_case__							)			,					make_atomaa_masks(snake_case__							)							)
			return out



